2,630,764 research outputs found

    High p_T Hadron Correlation and No Correlation

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    An overview of the correlation among high-p_T hadrons produced in heavy-ion collisions is presented. Emphasis is placed on the physical processes that can quantitatively account for the data on correlations in p_T, \eta and \phi on the near and far sides. Predictions are made on processes that have no observable correlations for hadrons produced at intermediate and higher p_T at RHIC and LHC.Comment: 8 pages including 14 figures. Talk given at Hard Probes 200

    Correlation-induced DNA adsorption on like-charged membranes

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    The adsorption of DNA or other polyelectrolyte molecules on charged membranes is a recurrent motif in soft matter and bionanotechnological systems. Two typical situations encountered are the deposition of single DNA chains onto substrates for further analysis, e.g., by force microscopy, or the pulling of polyelectrolytes into membrane nanopores, as in sequencing applications. In this paper, we present a theoretical analysis of such scenarios based on the self-consistent field theory approach, which allows us to address the important effect of charge correlations. We calculate the grand potential of a stiff polyelectrolyte immersed in an electrolyte in contact with a negatively charged dielectric membrane. For the sake of conciseness, we neglect conformational polymer fluctuations and model the molecule as a rigid charged line. At strongly charged membranes, the adsorbed counterions enhance the screening ability of the interfacial region. In the presence of highly charged polymers such as double-stranded DNA molecules close to the membrane, this enhanced interfacial screening dominates the mean-field level DNA-membrane repulsion and results in the adsorption of the DNA molecule to the surface. This picture provides a simple explanation for the recently observed DNA binding onto similarly charged substrates [G. L.-Caballero et al., Soft Matter 10, 2805 (2014)] and points out charge correlations as a non-negligible ingredient of polymer-surface interactions

    Billiards correlation functions

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    We discuss various experiments on the time decay of velocity autocorrelation functions in billiards. We perform new experiments and find results which are compatible with an exponential mixing hypothesis, first put forward by [FM]: they do not seem compatible with the stretched exponentials believed, in spite of [FM], to describe the mixing. The analysis led us to several byproducts: we obtain information about the normal diffusive nature of the motion and we consider the probability distribution of the number of collisions in time tmt_m (as t_m\to\io) finding a strong dependence on some geometric characteristics of the locus of the billiards obstacles.Comment: 25 pages, 27 figures, POSTSCRIPT, not encoded, 730K. Keywords: Billiards, correlation functions, velocity autocorrelation, diffusion coefficients, Lorentz model, mixing, ergodic theory, chaos, Lyapunov exponents, numerical experiment

    Robust Correlation Clustering

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    In this paper, we introduce and study the Robust-Correlation-Clustering problem: given a graph G = (V,E) where every edge is either labeled + or - (denoting similar or dissimilar pairs of vertices), and a parameter m, the goal is to delete a set D of m vertices, and partition the remaining vertices V D into clusters to minimize the cost of the clustering, which is the sum of the number of + edges with end-points in different clusters and the number of - edges with end-points in the same cluster. This generalizes the classical Correlation-Clustering problem which is the special case when m = 0. Correlation clustering is useful when we have (only) qualitative information about the similarity or dissimilarity of pairs of points, and Robust-Correlation-Clustering equips this model with the capability to handle noise in datasets. In this work, we present a constant-factor bi-criteria algorithm for Robust-Correlation-Clustering on complete graphs (where our solution is O(1)-approximate w.r.t the cost while however discarding O(1) m points as outliers), and also complement this by showing that no finite approximation is possible if we do not violate the outlier budget. Our algorithm is very simple in that it first does a simple LP-based pre-processing to delete O(m) vertices, and subsequently runs a particular Correlation-Clustering algorithm ACNAlg [Ailon et al., 2005] on the residual instance. We then consider general graphs, and show (O(log n), O(log^2 n)) bi-criteria algorithms while also showing a hardness of alpha_MC on both the cost and the outlier violation, where alpha_MC is the lower bound for the Minimum-Multicut problem